\begin{abstract}
Data deduplication is important  for snapshot backup of virtual machines (VMs)
because of excessive redundant content.  Fingerprint search for source-side duplicate 
detection is resource intensive when the backup service for VMs  is co-located with  other 
cloud services.  This paper proposes a low-profile VM-centric backup service with  a tradeoff 
for a competitive  deduplication efficiency while using small computing resources, 
suitable for running on a converged cloud architecture that cohosts  many other services.
The key techniques in the proposed scheme are to localize deduplication as much as 
possible within each VM guided by similarity search, restrict global 
deduplication under popular chunks with extra replication support,
and use an approximate method for fast and simplified snapshot deletion.
This VM-centric design also improves the snapshot availability  during machine failures  
by separating popular chunks and associating  file system blocks with one VM for non-popular chunks.
This  paper  describes an evaluation of this VM-centric scheme to assess  its deduplication 
efficiency, resource usage,  and fault tolerance.

% Collocating a cluster-based duplicate service with other cloud services
%   necessary to eliminate
%redundant blocks and reduce cost. Collocating a cluster-based duplicate service with other cloud services

%A cloud environment that hosts a large number of virtual machines (VMs) has
%a high storage demand for frequent backup of system image snapshots. 
%Deduplication of data blocks is necessary to eliminate
%redundant blocks and reduce cost. Collocating a cluster-based duplicate service with other cloud services
%reduces network traffic;
%however, 
\end{abstract}
